Health and Public Health Applications for Decision Support Using Machine Learning

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 24189

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Faculty of Biotechnology, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
Interests: signal processing; image processing; machine learning; neurodegenerative diseases; EEG; ECG; voice; microcontrollers; monitoring and forecasting systems
Special Issues, Collections and Topics in MDPI journals
Department of Business Administration, University of Saint Joseph, Macau 999078, China
Interests: bioengineering; digital signal processing; image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Department of Computing, Federal University of Ceará, Fortaleza, Ceará, Brazil
Interests: digital signal processing; digital image processing; biosignal processing; machine learning for aiding medical diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Well-tuned sensors optimized to translate bioinformation into the digital world are constantly evolving. This kind of technology provides improved data on humans, monitors the influence of the environment on humans and public health, and provides an excellent tool supporting the diagnosis of several diseases. Such devices can acquire data from a wide range of sources; with the help of high-level signals and image processing techniques, they can extract valuable data to feed artificial intelligence system models, with the aim of supporting decision making in various areas. This Special Issue is dedicated to covering the latest findings and emerging concepts relating to sensors and machine learning applications in health and public health areas. Topics may include, but are not limited to, the following:

  • Signals: EEG, EMG, ECG, and evoked potential analysis;
  • Medical imaging: X-ray, PET, CT, MRI, and SPECT;
  • Epidemiological and environment data: temperature, humidity, NOx, pH, VOC signals, and epidemiological time series and their relationship with health and public health;
  • Wearable applications.

Prof. Dr. Pedro Miguel Rodrigues
Prof. Dr. João Alexandre Lobo Marques
Prof. Dr. João Paulo do Vale Madeiro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • signal and image processing
  • health applications
  • public heath applications
  • artificial intelligence system models
  • machine learning
  • decision making

Published Papers (13 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

5 pages, 208 KiB  
Editorial
Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices
by Pedro Miguel Rodrigues, João Paulo Madeiro and João Alexandre Lobo Marques
Bioengineering 2023, 10(7), 792; https://doi.org/10.3390/bioengineering10070792 - 02 Jul 2023
Cited by 2 | Viewed by 1373
Abstract
In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...] Full article

Research

Jump to: Editorial, Review

24 pages, 4382 KiB  
Article
Biomedical Relation Extraction Using Dependency Graph and Decoder-Enhanced Transformer Model
by Seonho Kim, Juntae Yoon and Ohyoung Kwon
Bioengineering 2023, 10(5), 586; https://doi.org/10.3390/bioengineering10050586 - 12 May 2023
Cited by 2 | Viewed by 1512
Abstract
The identification of drug–drug and chemical–protein interactions is essential for understanding unpredictable changes in the pharmacological effects of drugs and mechanisms of diseases and developing therapeutic drugs. In this study, we extract drug-related interactions from the DDI (Drug–Drug Interaction) Extraction-2013 Shared Task dataset [...] Read more.
The identification of drug–drug and chemical–protein interactions is essential for understanding unpredictable changes in the pharmacological effects of drugs and mechanisms of diseases and developing therapeutic drugs. In this study, we extract drug-related interactions from the DDI (Drug–Drug Interaction) Extraction-2013 Shared Task dataset and the BioCreative ChemProt (Chemical–Protein) dataset using various transfer transformers. We propose BERTGAT that uses a graph attention network (GAT) to take into account the local structure of sentences and embedding features of nodes under the self-attention scheme and investigate whether incorporating syntactic structure can help relation extraction. In addition, we suggest T5slim_dec, which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the relation classification problem by removing the self-attention layer in the decoder block. Furthermore, we evaluated the potential of biomedical relation extraction of GPT-3 (Generative Pre-trained Transformer) using GPT-3 variant models. As a result, T5slim_dec, which is a model with a tailored decoder designed for classification problems within the T5 architecture, demonstrated very promising performances for both tasks. We achieved an accuracy of 91.15% in the DDI dataset and an accuracy of 94.29% for the CPR (Chemical–Protein Relation) class group in ChemProt dataset. However, BERTGAT did not show a significant performance improvement in the aspect of relation extraction. We demonstrated that transformer-based approaches focused only on relationships between words are implicitly eligible to understand language well without additional knowledge such as structural information. Full article
Show Figures

Figure 1

22 pages, 888 KiB  
Article
Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion
by Heydar Khadem, Hoda Nemat, Jackie Elliott and Mohammed Benaissa
Bioengineering 2023, 10(4), 487; https://doi.org/10.3390/bioengineering10040487 - 19 Apr 2023
Cited by 6 | Viewed by 2205
Abstract
Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. [...] Read more.
Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis’s congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis. Full article
Show Figures

Figure 1

11 pages, 668 KiB  
Article
Walking Stability and Risk of Falls
by Arunee Promsri, Prasit Cholamjiak and Peter Federolf
Bioengineering 2023, 10(4), 471; https://doi.org/10.3390/bioengineering10040471 - 12 Apr 2023
Cited by 9 | Viewed by 1282
Abstract
Walking stability is considered a necessary physical performance for preserving independence and preventing falls. The current study investigated the correlation between walking stability and two clinical markers for falling risk. Principal component analysis (PCA) was applied to extract the three-dimensional (3D) lower-limb kinematic [...] Read more.
Walking stability is considered a necessary physical performance for preserving independence and preventing falls. The current study investigated the correlation between walking stability and two clinical markers for falling risk. Principal component analysis (PCA) was applied to extract the three-dimensional (3D) lower-limb kinematic data of 43 healthy older adults (69.8 ± 8.5 years, 36 females) into a set of principal movements (PMs), showing different movement components/synergies working together to accomplish the walking task goal. Then, the largest Lyapunov exponent (LyE) was applied to the first five PMs as a measure of stability, with the interpretation that the higher the LyE, the lower the stability of individual movement components. Next, the fall risk was determined using two functional motor tests—a Short Physical Performance Battery (SPPB) and a Gait Subscale of Performance-Oriented Mobility Assessment (POMA-G)—of which the higher the test score, the better the performance. The main results show that SPPB and POMA-G scores negatively correlate with the LyE seen in specific PMs (p ≤ 0.009), indicating that increasing walking instability increases the fall risk. The current findings suggest that inherent walking instability should be considered when assessing and training the lower limbs to reduce the risk of falling. Full article
Show Figures

Graphical abstract

15 pages, 8093 KiB  
Article
Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists
by Seyedehnafiseh Mirniaharikandehei, Alireza Abdihamzehkolaei, Angel Choquehuanca, Marco Aedo, Wilmer Pacheco, Laura Estacio, Victor Cahui, Luis Huallpa, Kevin Quiñonez, Valeria Calderón, Ana Maria Gutierrez, Ana Vargas, Dery Gamero, Eveling Castro-Gutierrez, Yuchen Qiu, Bin Zheng and Javier A. Jo
Bioengineering 2023, 10(3), 321; https://doi.org/10.3390/bioengineering10030321 - 02 Mar 2023
Cited by 1 | Viewed by 1613
Abstract
Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. [...] Read more.
Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. Results: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. Conclusion: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice. Full article
Show Figures

Figure 1

24 pages, 6231 KiB  
Article
Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application
by Ahmed Barnawi, Mehrez Boulares and Rim Somai
Bioengineering 2023, 10(3), 294; https://doi.org/10.3390/bioengineering10030294 - 26 Feb 2023
Cited by 3 | Viewed by 1555
Abstract
The World Health Organization (WHO) highlights that cardiovascular diseases (CVDs) are one of the leading causes of death globally, with an estimated rise to over 23.6 million deaths by 2030. This alarming trend can be attributed to our unhealthy lifestyles and lack of [...] Read more.
The World Health Organization (WHO) highlights that cardiovascular diseases (CVDs) are one of the leading causes of death globally, with an estimated rise to over 23.6 million deaths by 2030. This alarming trend can be attributed to our unhealthy lifestyles and lack of attention towards early CVD diagnosis. Traditional cardiac auscultation, where a highly qualified cardiologist listens to the heart sounds, is a crucial diagnostic method, but not always feasible or affordable. Therefore, developing accessible and user-friendly CVD recognition solutions can encourage individuals to integrate regular heart screenings into their routine. Although many automatic CVD screening methods have been proposed, most of them rely on complex prepocessing steps and heart cycle segmentation processes. In this work, we introduce a simple and efficient approach for recognizing normal and abnormal PCG signals using Physionet data. We employ data selection techniques such as kernel density estimation (KDE) for signal duration extraction, signal-to-noise Ratio (SNR), and GMM clustering to improve the performance of 17 pretrained Keras CNN models. Our results indicate that using KDE to select the appropriate signal duration and fine-tuning the VGG19 model results in excellent classification performance with an overall accuracy of 0.97, sensitivity of 0.946, precision of 0.944, and specificity of 0.946. Full article
Show Figures

Figure 1

14 pages, 5289 KiB  
Article
A Novel and Noninvasive Risk Assessment Score and Its Child-to-Adult Trajectories to Screen Subclinical Renal Damage in Middle Age
by Chen Chen, Guanzhi Liu, Chao Chu, Wenling Zheng, Qiong Ma, Yueyuan Liao, Yu Yan, Yue Sun, Dan Wang and Jianjun Mu
Bioengineering 2023, 10(2), 257; https://doi.org/10.3390/bioengineering10020257 - 15 Feb 2023
Cited by 1 | Viewed by 1122
Abstract
This study aimed to develop a noninvasive, economical and effective subclinical renal damage (SRD) risk assessment tool to identify high-risk asymptomatic people from a large-scale population and improve current clinical SRD screening strategies. Based on the Hanzhong Adolescent Hypertension Cohort, SRD-associated variables were [...] Read more.
This study aimed to develop a noninvasive, economical and effective subclinical renal damage (SRD) risk assessment tool to identify high-risk asymptomatic people from a large-scale population and improve current clinical SRD screening strategies. Based on the Hanzhong Adolescent Hypertension Cohort, SRD-associated variables were identified and the SRD risk assessment score model was established and further validated with machine learning algorithms. Longitudinal follow-up data were used to identify child-to-adult SRD risk score trajectories and to investigate the relationship between different trajectory groups and the incidence of SRD in middle age. Systolic blood pressure, diastolic blood pressure and body mass index were identified as SRD-associated variables. Based on these three variables, an SRD risk assessment score was developed, with excellent classification ability (AUC value of ROC curve: 0.778 for SRD estimation, 0.729 for 4-year SRD risk prediction), calibration (Hosmer—Lemeshow goodness-of-fit test p = 0.62 for SRD estimation, p = 0.34 for 4-year SRD risk prediction) and more potential clinical benefits. In addition, three child-to-adult SRD risk assessment score trajectories were identified: increasing, increasing-stable and stable. Further difference analysis and logistic regression analysis showed that these SRD risk assessment score trajectories were highly associated with the incidence of SRD in middle age. In brief, we constructed a novel and noninvasive SRD risk assessment tool with excellent performance to help identify high-risk asymptomatic people from a large-scale population and assist in SRD screening. Full article
Show Figures

Figure 1

16 pages, 597 KiB  
Article
ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System
by Jackson Henrique Braga da Silva, Paulo Cesar Cortez, Senthil K. Jagatheesaperumal and Victor Hugo C. de Albuquerque
Bioengineering 2023, 10(1), 115; https://doi.org/10.3390/bioengineering10010115 - 13 Jan 2023
Cited by 2 | Viewed by 1643
Abstract
Measurement uncertainty is one of the widespread concepts applied in scientific works, particularly to estimate the accuracy of measurement results and to evaluate the conformity of products and processes. In this work, we propose a methodology to analyze the performance of measurement systems [...] Read more.
Measurement uncertainty is one of the widespread concepts applied in scientific works, particularly to estimate the accuracy of measurement results and to evaluate the conformity of products and processes. In this work, we propose a methodology to analyze the performance of measurement systems existing in the design phases, based on a probabilistic approach, by applying the Monte Carlo method (MCM). With this approach, it is feasible to identify the dominant contributing factors of imprecision in the evaluated system. In the design phase, this information can be used to identify where the most effective attention is required to improve the performance of equipment. This methodology was applied over a simulated electrocardiogram (ECG), for which a measurement uncertainty of the order of 3.54% of the measured value was estimated, with a confidence level of 95%. For this simulation, the ECG computational model was categorized into two modules: the preamplifier and the final stage. The outcomes of the analysis show that the preamplifier module had a greater influence on the measurement results over the final stage module, which indicates that interventions in the first module would promote more significant performance improvements in the system. Finally, it was identified that the main source of ECG measurement uncertainty is related to the measurand, focused towards the objective of better characterization of the metrological behavior of the measurements in the ECG. Full article
Show Figures

Figure 1

8 pages, 374 KiB  
Communication
Distinction of Different Colony Types by a Smart-Data-Driven Tool
by Pedro Miguel Rodrigues, Pedro Ribeiro and Freni Kekhasharú Tavaria
Bioengineering 2023, 10(1), 26; https://doi.org/10.3390/bioengineering10010026 - 24 Dec 2022
Cited by 2 | Viewed by 1642
Abstract
Background: Colony morphology (size, color, edge, elevation, and texture), as observed on culture media, can be used to visually discriminate different microorganisms. Methods: This work introduces a hybrid method that combines standard pre-trained CNN keras models and classical machine-learning models for supporting colonies [...] Read more.
Background: Colony morphology (size, color, edge, elevation, and texture), as observed on culture media, can be used to visually discriminate different microorganisms. Methods: This work introduces a hybrid method that combines standard pre-trained CNN keras models and classical machine-learning models for supporting colonies discrimination, developed in Petri-plates. In order to test and validate the system, images of three bacterial species (Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus) cultured in Petri plates were used. Results: The system demonstrated the following Accuracy discrimination rates between pairs of study groups: 92% for Pseudomonas aeruginosa vs. Staphylococcus aureus, 91% for Escherichia coli vs. Staphylococcus aureus and 84% Escherichia coli vs. Pseudomonas aeruginosa. Conclusions: These results show that combining deep-learning models with classical machine-learning models can help to discriminate bacteria colonies with good accuracy ratios. Full article
Show Figures

Figure 1

22 pages, 12766 KiB  
Article
Audio-Visual Stress Classification Using Cascaded RNN-LSTM Networks
by Megha V. Gupta, Shubhangi Vaikole, Ankit D. Oza, Amisha Patel, Diana Petronela Burduhos-Nergis and Dumitru Doru Burduhos-Nergis
Bioengineering 2022, 9(10), 510; https://doi.org/10.3390/bioengineering9100510 - 27 Sep 2022
Cited by 4 | Viewed by 1930
Abstract
The purpose of this research is to emphasize the importance of mental health and contribute to the overall well-being of humankind by detecting stress. Stress is a state of strain, whether it be mental or physical. It can result from anything that frustrates, [...] Read more.
The purpose of this research is to emphasize the importance of mental health and contribute to the overall well-being of humankind by detecting stress. Stress is a state of strain, whether it be mental or physical. It can result from anything that frustrates, incenses, or unnerves you in an event or thinking. Your body’s response to a demand or challenge is stress. Stress affects people on a daily basis. Stress can be regarded as a hidden pandemic. Long-term (chronic) stress results in ongoing activation of the stress response, which wears down the body over time. Symptoms manifest as behavioral, emotional, and physical effects. The most common method involves administering brief self-report questionnaires such as the Perceived Stress Scale. However, self-report questionnaires frequently lack item specificity and validity, and interview-based measures can be time- and money-consuming. In this research, a novel method used to detect human mental stress by processing audio-visual data is proposed. In this paper, the focus is on understanding the use of audio-visual stress identification. Using the cascaded RNN-LSTM strategy, we achieved 91% accuracy on the RAVDESS dataset, classifying eight emotions and eventually stressed and unstressed states. Full article
Show Figures

Figure 1

13 pages, 2062 KiB  
Article
Early Diagnosis of Intracranial Internal Carotid Artery Stenosis Using Extracranial Hemodynamic Indices from Carotid Doppler Ultrasound
by Xiangdong Zhang, Dan Wu, Hongye Li, Yonghan Fang, Huahua Xiong and Ye Li
Bioengineering 2022, 9(9), 422; https://doi.org/10.3390/bioengineering9090422 - 29 Aug 2022
Cited by 2 | Viewed by 2241
Abstract
Atherosclerotic intracranial internal carotid artery stenosis (IICAS) is a leading cause of strokes. Due to the limitations of major cerebral imaging techniques, the early diagnosis of IICAS remains challenging. Clinical studies have revealed that arterial stenosis may have complicated effects on the blood [...] Read more.
Atherosclerotic intracranial internal carotid artery stenosis (IICAS) is a leading cause of strokes. Due to the limitations of major cerebral imaging techniques, the early diagnosis of IICAS remains challenging. Clinical studies have revealed that arterial stenosis may have complicated effects on the blood flow’s velocity from a distance. Therefore, based on a patient-specific one-dimensional hemodynamic model, we quantitatively investigated the effects of IICAS on extracranial internal carotid artery (ICA) flow velocity waveforms to identify sensitive hemodynamic indices for IICAS diagnoses. Classical hemodynamic indices, including the peak systolic velocity (PSV), end-diastolic velocity (EDV), and resistive index (RI), were calculated on the basis of simulations with and without IICAS. In addition, the first harmonic ratio (FHR), which is defined as the ratio between the first harmonic amplitude and the sum of the amplitudes of the 1st–20th order harmonics, was proposed to evaluate flow waveform patterns. To investigate the diagnostic performance of the indices, we included 52 patients with mild-to-moderate IICAS (<70%) in a case–control study and considered 24 patients without stenosis as controls. The simulation analyses revealed that the existence of IICAS dramatically increased the FHR and decreased the PSV and EDV in the same patient. Statistical analyses showed that the average PSV, EDV, and RI were lower in the stenosis group than in the control group; however, there were no significant differences (p > 0.05) between the two groups, except for the PSV of the right ICA (p = 0.011). The FHR was significantly higher in the stenosis group than in the control group (p < 0.001), with superior diagnostic performance. Taken together, the FHR is a promising index for the early diagnosis of IICAS using carotid Doppler ultrasound methods. Full article
Show Figures

Graphical abstract

Review

Jump to: Editorial, Research

14 pages, 425 KiB  
Review
COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review
by Pedro Ribeiro, João Alexandre Lobo Marques and Pedro Miguel Rodrigues
Bioengineering 2023, 10(2), 198; https://doi.org/10.3390/bioengineering10020198 - 03 Feb 2023
Cited by 2 | Viewed by 1638
Abstract
Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based [...] Read more.
Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease. Full article
Show Figures

Figure 1

15 pages, 1120 KiB  
Review
Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review
by Gopi Battineni, Nalini Chintalapudi, Mohammad Amran Hossain, Giuseppe Losco, Ciro Ruocco, Getu Gamo Sagaro, Enea Traini, Giulio Nittari and Francesco Amenta
Bioengineering 2022, 9(8), 370; https://doi.org/10.3390/bioengineering9080370 - 05 Aug 2022
Cited by 16 | Viewed by 3099
Abstract
Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is [...] Read more.
Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age. Full article
Show Figures

Figure 1

Back to TopTop